Mass Spectrometry Techniques and Applications

3D Structure Determination of Protein Complexes using Matrix-Landing Mass Spectrometry

This paper introduces a modified mass spectrometry system for depositing gas-phase protein complexes onto EM grids, enabling 3D structure determination of non-covalent protein complexes like GroEL.

msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks

The paper introduces msiPL, a deep learning model for analyzing MSI data, enabling efficient non-linear visualization and identification of biologically relevant patterns.

Probing universal protein dynamics using residue-level Gibbs free energy

The paper introduces PyHDX, a tool for analyzing HDX-MS data to compute residue-level Gibbs free energy across protein conditions and homologues.

MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules

molDiscovery improves small molecule identification by learning fragmentation patterns from mass spectra, achieving higher efficiency and accuracy than existing methods.

Sequence-specific model for predicting peptide collision cross-section values in proteomic ion mobility spectrometry

The paper develops a sequence-specific model to predict peptide collision cross-section values using amino-acid sequence and charge state information from proteomic data.

Multi-Dimensional Reduction Clustering of Complex Carbohydrates Reveal Tissue Metabolism, Heterogeneity and Histopathology

The paper introduces Madi, a workflow combining MALDI-MSI and machine learning to analyze FFPE tissues, revealing metabolic patterns and pathological features in human tissues.

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

This paper introduces an automated workflow using SHAP to identify biomarker candidates in imaging mass spectrometry data by ranking molecular species based on their predictive importance for biological class recognition.

MS2DeepScore - a novel deep learning similarity measure for mass fragmentation spectrum comparisons

MS2DeepScore uses a Siamese neural network to predict structural similarity from mass fragmentation spectra, outperforming classical methods and enabling chemically meaningful spectral embeddings.

Screening for Potential Interaction Partners with Surface Plasmon Resonance Imaging Coupled to MALDI Mass Spectrometry

The paper couples SPR imaging with MALDI MS to identify RNA-binding proteins from yeast mitochondrial lysate, revealing new cofactors for intron splicing beyond Mss116.

pDeep3: Towards More Accurate Spectrum Prediction with Fast Few-Shot Learning

pDeep3 improves spectrum prediction accuracy using few-shot learning with minimal computational resources across different mass spectrometry instruments.

Quench me if you can: Alpha-2-macroglobulin trypsin complexes enable serum biomarker analysis by MALDI mass spectrometry

This paper describes a method to measure A2M concentration in human serum using MALDI mass spectrometry by analyzing the release of a specific peptide after trypsin interaction.

In Vivo Subcellular Mass Spectrometry Enables Proteo-Metabolomic Single-cell Systems Biology in a Chordate Embryo Developing to a Normally Behaving Tadpole (X. laevis)

The paper introduces in vivo subcellular mass spectrometry to analyze proteo-metabolomic profiles in Xenopus laevis embryos, revealing differential quantitative activities between cell types.

Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning

This paper uses deep learning to improve clustering of mass spectrometry imaging data by transforming ion images into neural ion images, resulting in more consistent and fine-grained spatial clusters.

Deep learning the collisional cross sections of the peptide universe from a million training samples

The paper measures CCS values for a million peptides across five organisms and develops a method to predict CCS for any peptide and organism.

Minimizing ion competition boosts volatile metabolome analysis by secondary electrospray ionization orbitrap mass spectrometry

This paper improves volatile metabolome analysis using SESI-Orbitrap MS by minimizing ion competition through optimized m/z window splitting.

Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification

This paper introduces a probabilistic framework that integrates mass spectrometry and retention time data to improve small molecule identification in LC-MS experiments.

ZODIAC: database-independent molecular formula annotation using Gibbs sampling reveals unknown small molecules

ZODIAC improves molecular formula annotation in metabolomics by combining SIRIUS results with Gibbs sampling, reducing errors and discovering novel compounds.

rMSIcleanup: An open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization

rMSIcleanup annotates and removes matrix-related peaks in MSI data, improving post-processing by reducing redundant signals.

Predicting Molecular Fingerprint from Electron-Ionization Mass Spectrum with Deep Neural Networks

The paper introduces DeepEI, a deep learning method that predicts molecular fingerprints from EI-MS spectra to improve compound identification accuracy.

Masserstein: robust linear deconvolution by optimal transport

The paper introduces a linear deconvolution method using optimal transport to improve accuracy in mass spectrometry by reducing sensitivity to measurement inaccuracies.

Ultraviolet Photodissociation of tryptic peptide backbones at 213 nm

The paper analyzes UVPD fragmentation of tryptic peptides at 213 nm, finding that sequence coverage and fragment intensity correlate with precursor charge density and that aromatic amino acids enhance fragmentation.

Trapped ion mobility spectrometry (TIMS) and parallel accumulation - serial fragmentation (PASEF) enable in-depth lipidomics from minimal sample amounts

This paper develops a high-sensitivity lipidomics workflow using TIMS and PASEF, enabling deeper lipid analysis from minimal samples with improved speed and coverage.

Mass Spectrometry Imaging of Cocaine, Drug Removal and Methylphenidate AlterPhospholipids in Drosophila Brain

The paper investigates how cocaine and methylphenidate affect brain phospholipids in Drosophila using mass spectrometry imaging, showing persistent changes and partial reversal with methylphenidate.

Accessible and reproducible mass spectrometry imaging data analysis in Galaxy

The paper integrates mass spectrometry imaging tools into Galaxy to enable accessible, reproducible, and transparent analysis of MSI data.

High Spatial Resolution Ambient Ionization Mass Spectrometry Imaging Using Microscopy Image Fusion Determines Tumor Margins

This study combines ambient ionization mass spectrometry imaging with microscopy to improve tumor margin detection in mouse tissues by identifying potential biomarkers.

Protocol for Community-created Public MS/MS Reference Library Within the GNPS Infrastructure

The paper describes a workflow for creating MS/MS reference spectra in the GNPS infrastructure, achieving 99.2% accurate annotation of chemical standards.

Imaging Sterols and Oxysterols in Mouse Brain Reveals Distinct Spatial Cholesterol Metabolism

The paper develops a mass spectrometry imaging method to map sterol distributions in mouse brain tissue, revealing spatial cholesterol metabolism patterns in wild-type and cholesterol 24S-hydroxylase knock-out mice.

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

ColocAI quantifies co-localization between mass spectrometry images using AI, outperforming existing methods and providing insights into 10,273 molecules from public datasets.

Methods for Analyzing Imaging Mass Cytometry Data with Tellurium Probes

The paper develops methods to improve image processing for tellurium-based imaging mass cytometry data by addressing background noise and signal-to-noise ratio issues.

Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging

The paper introduces a method using ion co-localization features to classify tumors from MSI data, addressing batch effects and generating hypotheses about sample classes.